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January 26, 2017


TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it. The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks… In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques and it is currently in its early development stage. TFLearn requires Tensorflow (version >= 0.9.0) to be installed. The easiest way [...]

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January 26, 2017


Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. Users can find the source on GitHub. Darknet is installed with only two optional dependencies: OpenCV if users want a wider variety of supported image types or CUDA if they want GPU computation. Neither is compulsory but users can start by just installing the base system which has only been tested on Linux and Mac computers. The framework features You Only Look Once (YOLO), a state-of-the-art, real-time object detection system. On a Titan X it processes images at 40-90 FPS and has a mAP on VOC 2007 of 78.6% and a mAP [...]

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January 26, 2017


NeuralN is a C++ Neural Network library for Node.js with multiple advantages compared to existing solutions. It works with extra-large datasets (>1Go allowed by nodejs) and Multi-Threaded training available. With Node.js and the V8, it is not possible to work with large datasets since the maximum allowed memory is around 512MB for 32-bits machines and 1GB for 64-bits machines. When working with datasets of several gigabytes, it quickly becomes difficult to train the network with all the data. NeuralN allows users to use datasets as big as their memory can contain. Working with large datasets increases the performances of the final network, but the [...]

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January 26, 2017


ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in users’ browsers. Users just open a tab and they are training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows users to formulate and solve Neural Networks in Javascript, and was originally written by @karpathy (a PhD student at Stanford). However, the library has since been extended by contributions from the community. The code is available on Github under MIT license. Pull requests for new features / layers / demos and miscellaneous improvements are encouraged. The library is also available on npm for use in [...]

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January 26, 2017


Synaptic is a javascript neural network library for node.js and the browser, its generalized algorithm is architecture-free, so users can build and train basically any type of first order or even second order neural network architectures. This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an Embedded Reber Grammar test, so users can easily test and compare the [...]

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January 26, 2017


AForge.NET framework provides neural networks library, which contains set of classes aimed for creating different type of artificial neural networks and training them to solve certain tasks, like recognition, approximation, prediction, etc. The library mainly allows users to create two categories of artificial neural networks: feed forward neural networks with activation function and one layer distance networks. Feed forward neural networks are represented as one layer or as multi-layer networks which don’t have recurrent connections. Information flows in these networks from inputs to outputs passing all layers of neural network only one time [...]

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January 26, 2017


The version 1.0.0 of gobrain includes just basic Neural Network functions such as Feed Forward and Elman Recurrent Neural Network. Using the framework, users are able to construct a simple Feed Forward Neural Network by first creating the XOR representation pattern to train the network. The networks structure should contain a specific number of inputs, hidden nodes and outputs. The training should run for a given number of epochs e.g. 1000. The learning rate can be set to 0.6 and the momentum factor to 0.4. Users can receive reports about the learning error by using true in the last parameter. The BackPropagate method is used when training the Neural [...]

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January 26, 2017


DN2A is a set of highly decoupled JavaScript modules for Neural Networks and Artificial Intelligence development. Each module is based on injection by configuration. Clients can use a single module alone, more of them together or just the complete set. DN2A’s main goal is to allow users to design, train and use Single Neural Networks with ease as well as very powerful Neural Networks Chains through which to implement their Artificial Intelligence solutions. DN2A’s side goals are to simplify integration, to speed up training/querying, to allow clustering and to represent the architecture and the relative data of each Neural Network as a (re)combinable [...]

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